The use of electronic devices to perform any number of tasks has steadily increased over time. This is especially true in the field of providing healthcare to patients. In the medical field, patient monitoring devices and/or systems are selectively coupled to a patient via at least one sensor, which senses information from the patient, which is used in deriving at least one physiological parameter associated with the patient.
Different types of patient monitoring devices are able to monitor the physiological state of the patient via at least one electrode applied to the skin of the patient at various locations on the body. For example, the electrical activity of the heart is routinely monitored in clinical environments using an electrocardiogram (ECG) monitor. The ECG monitor is connected to the patient via a plurality of electrodes (sensors) that monitor the electrical impulses emanating from the patient's heart. Wires from the monitor are selectively connected to the electrodes in order to communicate impulses/voltages detected to the ECG monitoring device to provide a healthcare practitioner with data regarding the patient's heart function.
A drawback associated with ECG monitoring relates to the environment in which the monitoring occurs. There are a number of potential sources of interference that exist in the clinical environment. These sources of interference may produce unacceptable noise levels, false beat detections, false beat classifications, and false alarms. While removing all such artifacts is near impossible, there have been attempts to generate and implement a number of artifact detection algorithms to suspend ECG beat detection (and other ECG parameter processing) in the presence of a variety of different types of noise. However, current ECG artifact detection mechanisms are not sensitive enough and result in false alarms caused by mistaking artifacts (such as those caused by patient movement and electrode connectivity issues) as heartbeats. For example, an artifact in the sensed signal may result in the ECG monitor falsely mistaking a portion of the signal for a heartbeat or falsely detecting/identifying the portion of the signal as a QRS complex. This misidentification may result in a number of false alarms including increased heart rate, high PVC runs, ventricular tachycardia, and others. The incorrect measures lead to false alarms and contribute to the clinical problem of alarm fatigue, wherein clinicians become desensitized to overactive alarms.
One approach for identifying artifacts in sensed signals is to discretely identify individual types of artifacts. For example, previous algorithms identify three types of artifacts—high-frequency, characterized by excessive spikes; low-frequency, characterized by large discontinuities; and baseline, characterized by gradual “wandering” away from the typical DC value. Examples of these can be seen in FIG. 1A-1C, respectively. To accomplish this, prior algorithms employ hard thresholds to time-domain features, each of which is designed to catch one and only one of these different artifact types.
As seen in FIG. 1A, “high-frequency” (HF) artifacts are characterized by large numbers of visible spikes, which can easily be mistaken for QRS complexes. This type of artifact is frequently caused by muscle artifacts from patient movements, and can also be caused by electrostatic discharge or power line interference. In the time domain, the number of spikes detected in a given interval can be used to detect these types of artifacts. The goal of a high frequency artifact detection algorithm is to detect a large number of spikes in a small interval.
In contrast to the numerous small spikes that characterize our “high-frequency” artifacts shown in FIG. 1A, low frequency (LF) artifacts can be identified by large jumps in baseline values as shown in FIG. 1B. These discontinuities do in fact produce energy at both high and low frequencies, but conventional LF artifact detection algorithms specifically identify the large displacement from 0. An exemplary LF artifact detection algorithm searches for any extended interval whose voltage is entirely above a fixed threshold over a predetermined period of time. This type of LF artifact detection algorithm is especially useful in detecting artifacts caused by poor electrode connections. A drawback associated with LF artifact detection relates to instances where large QRS complexes are present such as seen during premature ventricular contractions (PVC). One must be careful that these larger QRS complexes that may exceed the voltage threshold are not erroneously labeled as artifacts.
Baseline (BS) artifacts, as seen in FIG. 1C, are characterized by a slow change, often sinusoidal, in the isoline value of the ECG signal. However, even in the presence of large baseline artifacts, as seen in the given example, individual beats can still be readily identified both visually and by certain beat classification algorithms. Certain filtering techniques identify regions of rapid change (typical of QRS complexes) which greatly reduce the impact of this type of artifact. When a baseline artifact is detected, QRS classification and arrhythmia detection may be suppressed, but beat detection remains intact. The BS artifact detection algorithm is similar to LF artifact detection with the differences relating to the voltage thresholds against which the signal is compared. More specifically, the voltage threshold used in BS artifact detection is much lower than the threshold used in LF artifact detection (e.g. in LF artifact detection, the threshold may be ±2 mV whereas the threshold in BS artifact detection may be ±0.5 mV relative to the isoline). Additionally, an the amount of time the ECG signal must spend outside of the voltage thresholds is much longer than a QRS complex before a baseline artifact can be declared by the BS artifact detection algorithm.
Other artifact detection algorithms employ a weighted sum of two input channels used to detect QRS complexes. In these algorithms, the weights assigned to each channel are dependent on the respective waveform amplitudes and noise levels. These algorithms determine the best lead through a normalized distance metric applied to the area under the QRS complex, referred to as the “mismatch”. Once the mismatch values are calculated for each QRS complex, they are stored in a histogram structure. This allows the lead selection process to capture a snapshot of the consistency of the QRS complex over time.
Further artifact detection algorithms depend on a finite impulse response residual filter (FRF), which relies on two finite impulse response (FIR) filters—a highpass filter and a lowpass filter in series—to aggressively filter out the residual of the ECG after subtraction of the median beat. Once the ECG signal has been filtered, a switch is applied to select the two best quality leads for beat identification and arrhythmia analysis. Thus, the determination of quality relies on the amplitudes of the detected QRS complexes, the amount of noise present in the signal after filtering, and the electrode connectivity.
While the above list certain examples of artifact detection algorithms, each still maintain the known drawback of insufficient sensitivity resulting in a number of false alarms caused by mistaking artifacts such as those caused by patient movement and sensor connectivity issues as heartbeats. It is thus highly desirable to identify portions of the signals likely to contain artifacts and suspend patient parameter processing on those portions of the signal. A system according to the current subject matter addresses the deficiencies associated with deriving patient parameters from a signal sensed by a sensor.